U.S. patent number 10,205,317 [Application Number 14/821,115] was granted by the patent office on 2019-02-12 for management of grid-scale energy storage systems for multiple services.
This patent grant is currently assigned to NEC Corporation. The grantee listed for this patent is NEC Laboratories America, Inc.. Invention is credited to Babak Asghari, Rakesh Patil, Ratnesh Sharma, Di Shi.
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United States Patent |
10,205,317 |
Sharma , et al. |
February 12, 2019 |
Management of grid-scale energy storage systems for multiple
services
Abstract
Systems and methods for energy distribution for one or more
grid-scale Energy Storage Systems (ESSs), including generating one
or more time series models to provide forecasted pricing data for
one or more markets, determining battery life and degradation costs
for one or more batteries in or more ESSs to provide battery life
and degradation costs, optimizing bids for the one or more markets
to generate optimal bids based on at least one of the forecasted
pricing data or the battery life and degradation costs, and
distributing energy to or from the one or more ESSs based on the
optimal bids generated.
Inventors: |
Sharma; Ratnesh (Fremont,
CA), Shi; Di (San Jose, CA), Asghari; Babak (San
Jose, CA), Patil; Rakesh (San Francisco, CA) |
Applicant: |
Name |
City |
State |
Country |
Type |
NEC Laboratories America, Inc. |
Princeton |
NJ |
US |
|
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Assignee: |
NEC Corporation
(JP)
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Family
ID: |
55268158 |
Appl.
No.: |
14/821,115 |
Filed: |
August 7, 2015 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20160043550 A1 |
Feb 11, 2016 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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62034844 |
Aug 8, 2014 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06Q
30/0206 (20130101); H02J 3/00 (20130101); H02J
7/35 (20130101); H02J 3/383 (20130101); G05B
13/041 (20130101); H02J 3/381 (20130101); H02J
3/003 (20200101); Y04S 50/14 (20130101); Y02E
60/00 (20130101); Y04S 40/20 (20130101); Y02E
10/56 (20130101); Y04S 10/50 (20130101); H02J
2203/20 (20200101); H02J 2300/24 (20200101) |
Current International
Class: |
H02J
3/00 (20060101); G06Q 30/02 (20120101); H02J
7/35 (20060101); G05B 13/04 (20060101); H02J
3/38 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Chan, et al., "Load/Price Forecasting and Managing Demand Response
for Smart Grids: Methodologies and challenges," IEEE Signal
Processing Magazine, Sep. 2012, vol. 29, No. 5, pp. 68-85. cited by
applicant .
Nogales, et al., "Forecasting Next-day Electricity Prices by Time
Series Models," IEEE Transactions Power System May 2002, vol. 17,
No. 2, pp. 342-348. cited by applicant .
Pindoriya, et al., "An Adaptive Wavelet Neural Network-Based Energy
Price Forecasting in Electricity Markets", IEEE Transactions on
Power Systems, Aug. 2008, vol. 23, No. 3, pp. 1423-1432. cited by
applicant .
Thatte, et al., "Towards a Unified Operational Value Index of
Energy Storage in Smart Grid Environment," IEEE Transactions on
Smart Grid, Sep. 2012, vol. 3, No. 3, pp. 1418-1426. cited by
applicant .
Weron, et al., "Forecasting Spot Electricity Prices With Time
Series Models," Presentation at the Hugo Steinhaus Center, Wroclaw,
Poland, 16 Pages, May 2005. cited by applicant .
Michael Ward, "Working to Perfect the Flow of Energy", PJM eMKT
User Guide, Jun. 2015, pp. 1-175. cited by applicant .
R. Treinen, Market Operations, California ISP MRTU, "Locational
Marginal Pricing (LMP): Basics of Nodal Price Calculation", CRR
Educational Class #2, CAISO Market Operations, Dec. 2005, pp. 1-95.
cited by applicant .
Trishna Das, "Performance and Economic Evaluation of Storage
Technologies", Iowa State University, Graduate Theses and
Dissertations, Jan. 2013, pp. 1-258. cited by applicant.
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Primary Examiner: Koneru; Sujay
Attorney, Agent or Firm: Kolodka; Joseph
Parent Case Text
RELATED APPLICATION INFORMATION
This application claims priority to provisional application number
62/034,844 filed Aug. 8, 2014, the contents of which are
incorporated herein by reference.
Claims
What is claimed is:
1. A computer implemented method for energy distribution for one or
more grid-scale Energy Storage Systems (ESSs), comprising:
controlling, using a tertiary controller: generating of one or more
time series models to provide forecasted pricing data for one or
more markets; determining of battery life and degradation costs for
one or more batteries in one or more ESSs to provide battery life
and degradation costs, the degradation costs being determined for
time step k as:
Cost(k)=C.sub.b|P.sub.b.sup.energy(k)|+C.sub.bP.sub.b.sup.reg(k),
where C.sub.b represents per unit degradation costs for the one or
more ESSs, P.sub.b.sup.energy(k) represents power of the one or
more ESSs for time step k, and P.sub.b.sup.reg(k) represents
capacity of the one or more ESSs in a frequency regulation (FR)
market; and optimizing of bids for the one or more markets to
generate optimal bids based on at least one of the forecasted
pricing data or the battery life and degradation costs; and
controlling, using a primary controller, distributing of energy to
or from the one or more ESSs based on the optimal bids, the primary
controller being configured for: regulating voltage at a point of
connection to a power grid from the one or more ESSs, the
regulating further comprising maintaining a stable voltage by
controlling, using a reactive power controller, reactive power
injection at one or more points of common coupling (PCCs) from the
one or more ESSs to the power grid; and controlling, using an
active power controller, real-time charge and discharge commands
based on the optimizing and regulating for real-time control of the
one or more ESSs.
2. The method of claim 1, wherein the one or more time series
models include a Locational Marginal Price (LMP) time series model
and a voltage regulation time series model.
3. The method of claim 1, wherein the determining battery life and
degradation costs is performed on an hourly basis for each of the
one or more ESSs, wherein the determining further comprises
generating an average model based on per unit degradation costs of
each ESS multiplied by the energy throughput of the ESS.
4. The method of claim 1, wherein the optimizing further comprises
performing stochastic optimization to evaluate a cost tradeoff of
providing energy services vs. battery life costs for a plurality of
situations to determine an optimal battery dispatch, wherein the
one or more markets include an energy market and a frequency
market.
5. The method of claim 1, wherein the controlling further comprises
actively controlling power to dispatch ESSs automatically to
maintain a system voltage within a normal range.
6. The method of claim 1, wherein the regulating controls the one
or more ESSs to reduce system loss and save energy using
conservation voltage regulation (CVR).
7. The method of claim 1, wherein the bids are day-ahead energy
bids.
8. A system for energy distribution for one or more grid-scale
Energy Storage Systems (ESSs), comprising: a tertiary controller,
comprising: a forecaster, coupled to a bus, for generating one or
more time series models to predict pricing data for one or more
markets, with the one or more time series models being stored
therein; a determiner for providing battery life and degradation
costs for one or more batteries in the one or more ESSs, the
degradation costs being determined for time step k as:
Cost(k)=C.sub.b|P.sub.b.sup.energy(k)|+C.sub.bP.sub.b.sup.reg(k),
where C.sub.b represents per unit degradation costs for the one or
more ESSs, p.sub.b.sup.energy(k) represents power of the one or
more ESSs for time step k, and P.sub.b.sup.reg(k) represents
capacity of the one or more ESSs in a frequency regulation (FR)
market; and an optimizer, coupled to the bus, for generating
optimal bids for the one or more markets based on the pricing data
or the battery life and degradation costs for the one or more ESSs;
and a primary controller for distributing energy to or from the one
or more ESSs based on the optimal bids, the primary controller
comprising: a regulator coupled to the bus for voltage regulation
at a point of connection to a power grid from the one or more ESSs,
the regulator further comprising a reactive power controller
configured to maintain a stable voltage by controlling reactive
power injection at one or more points of common coupling (PCCs)
from the system to the power grid; and an active power control
configured to generate and issue real-time charge and discharge
commands based on the optimal bids and the voltage regulation for
real-time control of the one or more ESSs.
9. The system of claim 8, wherein the one or more time series
models include a Locational Marginal Price (LMP) time series model
and a voltage regulation time series model.
10. The system of claim 8, wherein the determiner provides battery
life and degradation costs on an hourly basis for each of the one
or more ESSs, and the battery life and degradation costs are
determined by generating an average model based on per unit
degradation costs of each ESS multiplied by the energy throughput
of the ESS.
11. The system of claim 8, wherein the optimizer performs
stochastic optimization to evaluate a cost tradeoff of providing
energy services vs. battery life costs for a plurality of
situations to determine an optimal battery dispatch.
12. The system of claim 8, wherein the primary controller actively
controls power to dispatch ESSs automatically to maintain system
voltage within a normal range.
13. The system of claim 8, wherein the regulator controls the one
or more ESSs to reduce system loss and save energy using
conservation voltage regulation (CVR).
14. The system of claim 8, wherein the tertiary controller performs
real-time system parameter identification to enable millisecond
level control of voltage regulation by the primary controller.
15. A non-transitory computer-readable storage medium comprising a
computer readable program for energy distribution for one or more
grid-scale Energy Storage Systems (ESSs), wherein the computer
readable program when executed on a computer causes the computer to
perform the steps of: controlling, using a tertiary controller:
generating of one or more time series models to provide forecasted
pricing data for one or more markets; determining of battery life
and degradation costs for one or more batteries in or more ESSs to
provide battery life and degradation costs, the degradation costs
being determined for time step k as:
Cost(k)=C.sub.b|P.sub.b.sup.energy(k)|+C.sub.bP.sub.b.sup.reg(k),
where C.sub.b represents per unit degradation costs for the one or
more ESSs, P.sub.b.sup.energy(k) represents power of the one or
more ESSs for time step k, and P.sub.b.sup.reg(k) represents
capacity of the one or more ESSs in a frequency regulation (FR)
market; and optimizing of bids for the one or more markets to
generate optimal bids based on at least one of the forecasted
pricing data or the battery life and degradation costs; and
controlling, using a primary controller, distributing of energy to
or from the one or more ESSs based on the optimal bids, the primary
controller being configured for: regulating voltage at a point of
connection to a power grid from the one or more ESSs, the
regulating further comprising maintaining a stable voltage by
controlling, using a reactive power controller, reactive power
injection at one or more points of common coupling (PCCs) from the
one or more ESSs to the power grid; and controlling, using an
active power controller, real-time charge and discharge commands
based on the optimizing and regulating for real-time control of the
one or more ESSs.
16. The non-transitory computer-readable storage medium of claim
15, wherein the determining battery life and degradation costs is
performed on an hourly basis for each of the one or more ESSs, and
is determined by generating an average model based on per unit
degradation costs of each ESS multiplied by the energy throughput
of the ESS.
Description
BACKGROUND OF THE INVENTION
Technical Field
The present invention relates generally to management of grid-scale
Energy Storage Systems (ESSs), and more particularly, to a hybrid
energy management system for dynamically controlling grid-scale
ESSs for multiple services.
Description of the Related Art
Grid-connected energy storage systems (ESSs) are a fast growing
global market. Recently, increases in the penetration of renewable
energy resources into grid-connected ESSs have presented a
challenge to the traditional design and operation of electric power
systems. The existing power grid was designed for centralized power
generation with unidirectional power flow. With renewable energy
(or any other type of distributed generation of electricity), power
is effectively generated everywhere and flows in multiple
directions. However, the intermittent and highly variable nature of
distributed generation causes power quality and/or reliability
issues, which leads to increased energy costs.
Research on forecasting electricity prices has focused on
techniques including employment of neural networks, principle
component analysis, averaged Monte Carlo simulations, and time
series modeling. Although these methods have been applied to obtain
price forecasts, the focus of these methods is simply to improve
forecasting quality through improved model fitting, and processing
costs and the practical application of the forecasting information
are not considered. Furthermore, these conventional forecasting
methods also require large amounts of data (e.g., several months,
years, etc.) for forecasting of electricity prices. Moreover, this
forecasting is not employed for participation in energy
markets.
SUMMARY
A computer implemented method for energy distribution for one or
more grid-scale Energy Storage Systems (ESSs), including generating
one or more time series models to provide forecasted pricing data
for one or more markets, determining battery life and degradation
costs for one or more batteries in or more ESSs to provide battery
life and degradation costs, optimizing bids for the one or more
markets to generate optimal bids based on at least one of the
forecasted pricing data or the battery life and degradation costs,
and distributing energy to or from the one or more ESSs based on
the optimal bids generated.
A system for energy distribution for one or more grid-scale Energy
Storage Systems (ESSs), including a forecaster, coupled to a bus,
for generating one or more time series models to predict pricing
data for one or more markets, with the one or more time series
models being stored therein, a determiner for providing battery
life and degradation costs for one or more batteries in the one or
more ESSs, an optimizer, coupled to the bus, for generating optimal
bids for the one or more markets based on the pricing data or the
battery life and degradation costs for the one or more ESSs, and a
controller for distributing energy to or from the one or more ESSs
based on the optimal bids.
A computer-readable storage medium including a computer-readable
program for energy distribution for one or more grid-scale Energy
Storage Systems (ESSs), wherein the computer-readable program when
executed on a computer causes the computer to perform the steps of
generating one or more time series models to provide forecasted
pricing data for one or more markets, determining battery life and
degradation costs for one or more batteries in or more ESSs to
provide battery life and degradation costs, optimizing bids for the
one or more markets to generate optimal bids based on at least one
of the forecasted pricing data or the battery life and degradation
costs, and distributing energy to or from the one or more ESSs
based on the optimal bids generated.
These and other advantages of the invention will be apparent to
those of ordinary skill in the art by reference to the following
detailed description and the accompanying drawings.
BRIEF DESCRIPTION OF DRAWINGS
The disclosure will provide details in the following description of
preferred embodiments with reference to the following figures
wherein:
FIG. 1 shows an exemplary processing system to which the present
principles may be applied, in accordance with an embodiment of the
present principles;
FIG. 2 shows an exemplary method for dynamically controlling
grid-scale Energy Storage Systems (ESSs), in accordance with an
embodiment of the present principles;
FIG. 3 shows an exemplary method for forecasting, in accordance
with an embodiment of the present principles;
FIG. 4 shows an exemplary power system and a simplified equivalent
model in accordance with an embodiment of the present
principles;
FIG. 5 shows an exemplary system and method for voltage regulation,
in accordance with an embodiment of the present principles;
FIG. 6 shows an exemplary method for voltage regulation, in
accordance with an embodiment of the present principles;
FIG. 7 shows an exemplary system and method for primary and
tertiary control of Energy Storage Systems (ESSs), in accordance
with an embodiment of the present principles; and
FIG. 8 shows an exemplary system for dynamically controlling
grid-scale Energy Storage Systems (ESSs), in accordance with an
embodiment of the present principles.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
The present principles are directed to systems and methods for
hybrid energy management for dynamically controlling grid-scale
Energy Storage Systems (ESSs) to improve reliability and reduce
energy costs according to various embodiments.
In an embodiment, a time series based market price forecasting
engine may be employed according to the present principles.
Forecasted prices may be employed in conjunction with a battery
degradation cost to schedule Grid Scale Storage (GSS) for
participation in multiple energy markets. In addition, a novel
voltage regulation method for use in GSS may advantageously be
employed according to some embodiments. In a particularly useful
embodiment, a portion of GSS capacity is excluded from market
co-optimization. This enables GSS to generate substantial revenue
from energy and Frequency Regulation (FR) markets, and also
provides additional services to the energy grid as a voltage
regulation provider in some embodiments.
In an embodiment, co-optimization (e.g., real-time co-optimization)
is employed to meet energy demands (e.g., energy market) and to
meet reserve requirements (e.g., reserve markets) by jointly
clearing the energy markets and reserve markets to minimize overall
costs. Co-optimization according to the present principles may be
employed to meet energy demands at a minimum cost while maintaining
system reliability. In an embodiment, co-optimization may be
employed to schedule the GSS charge and discharge operations to
maximize GSS revenue from participating in energy market and
reserve markets (e.g., including frequency regulation (FR) market).
Co-optimization may maximize GSS revenue from participating in
different markets according to the present principles.
In an embodiment, degradation costs associated with GSS operations
are considered and included when performing co-optimization because
this degradation costs may result in a considerable impact on the
optimal schedule of GSS in the markets. Dynamic constraints for
market scheduling and operation may be employed to achieve
co-optimization according to some embodiments of the present
principles. To participate in day-ahead electricity markets and
yield optimal revenues, a forecast or estimate of day-ahead prices
may be employed.
Prices in different electricity markets (e.g., energy, FR, etc.)
are known only after energy bids clear, and as such, the price
forecasting engine according the present principles may be employed
to participate in markets optimally. In an embodiment, the price
forecasting may be performed using a small amount of data (e.g.,
days) with low computational effort, and may include a time series
based forecasting method because this method is computationally
fast and may allow for the inclusion of exogenous inputs (e.g.,
forecasted load and its derivatives, derivatives of past price
etc.).
In an embodiment, a preventive control framework for voltage
regulation through real-time adaptive control of GSSs charging
and/or discharging may be implemented according to the present
principles. A real-time equivalent circuit of one or more power
systems may be established using voltage and/or current measured at
a point of common coupling (PCC). This equivalent circuit may be
employed to identify possible voltage violations in advance of any
violations, and one or more GSS actions may be determined and
performed to avoid any violations according to various embodiments
of the present principles.
It should be understood that embodiments described herein may be
entirely hardware or may include both hardware and software
elements, which includes but is not limited to firmware, resident
software, microcode, etc. In a preferred embodiment, the present
invention is implemented in hardware. The present invention may be
a system, a method, and/or a computer program product. The computer
program product may include a computer readable storage medium (or
media) having computer readable program instructions thereon for
causing a processor to carry out aspects of the present
invention.
Embodiments may include a computer program product accessible from
a computer-usable or computer-readable medium providing program
code for use by or in connection with a computer or any instruction
execution system. A computer-usable or computer readable medium may
include any apparatus that stores, communicates, propagates, or
transports the program for use by or in connection with the
instruction execution system, apparatus, or device. The medium can
be magnetic, optical, electronic, electromagnetic, infrared, or
semiconductor system (or apparatus or device) or a propagation
medium. The medium may include a computer-readable storage medium
such as a semiconductor or solid state memory, magnetic tape, a
removable computer diskette, a random access memory (RAM), a
read-only memory (ROM), a rigid magnetic disk and an optical disk,
etc.
A data processing system suitable for storing and/or executing
program code may include at least one processor coupled directly or
indirectly to memory elements through a system bus. The memory
elements can include local memory employed during actual execution
of the program code, bulk storage, and cache memories which provide
temporary storage of at least some program code to reduce the
number of times code is retrieved from bulk storage during
execution. Input/output or I/O devices (including but not limited
to keyboards, displays, pointing devices, etc.) may be coupled to
the system either directly or through intervening I/O
controllers.
Network adapters may also be coupled to the system to enable the
data processing system to become coupled to other data processing
systems or remote printers or storage devices through intervening
private or public networks. Modems, cable modem and Ethernet cards
are just a few of the currently available types of network
adapters.
Referring now to the drawings in which like numerals represent the
same or similar elements and initially to FIG. 1, an exemplary
processing system 100, to which the present principles may be
applied, is illustratively depicted in accordance with an
embodiment of the present principles. The processing system 100
includes at least one processor (CPU) 104 operatively coupled to
other components via a system bus 102. A cache 106, a Read Only
Memory (ROM) 108, a Random Access Memory (RAM) 110, an input/output
(I/O) adapter 120, a sound adapter 130, a network adapter 140, a
user interface adapter 150, and a display adapter 160, are
operatively coupled to the system bus 102.
A first storage device 122 and a second storage device 124 are
operatively coupled to system bus 102 by the I/O adapter 120. The
storage devices 122 and 124 can be any of a disk storage device
(e.g., a magnetic or optical disk storage device), a solid state
magnetic device, and so forth. The storage devices 122 and 124 can
be the same type of storage device or different types of storage
devices.
A speaker 132 is operatively coupled to system bus 102 by the sound
adapter 130. A transceiver 142 is operatively coupled to system bus
102 by network adapter 140. A display device 162 is operatively
coupled to system bus 102 by display adapter 160.
A first user input device 152, a second user input device 154, and
a third user input device 156 are operatively coupled to system bus
102 by user interface adapter 150. The user input devices 152, 154,
and 156 can be any of a keyboard, a mouse, a keypad, an image
capture device, a motion sensing device, a microphone, a device
incorporating the functionality of at least two of the preceding
devices, and so forth. Of course, other types of input devices can
also be used, while maintaining the spirit of the present
principles. The user input devices 152, 154, and 156 can be the
same type of user input device or different types of user input
devices. The user input devices 152, 154, and 156 are used to input
and output information to and from system 100.
Of course, the processing system 100 may also include other
elements (not shown), as readily contemplated by one of skill in
the art, as well as omit certain elements. For example, various
other input devices and/or output devices can be included in
processing system 100, depending upon the particular implementation
of the same, as readily understood by one of ordinary skill in the
art. For example, various types of wireless and/or wired input
and/or output devices can be used. Moreover, additional processors,
controllers, memories, and so forth, in various configurations can
also be utilized as readily appreciated by one of ordinary skill in
the art. These and other variations of the processing system 100
are readily contemplated by one of ordinary skill in the art given
the teachings of the present principles provided herein.
Moreover, it is to be appreciated that systems 400, 500, 700, and
800, described below with respect to FIGS. 4, 5, 7, and 8,
respectively, is a system for implementing respective embodiments
of the present principles. Part or all of processing system 100 may
be implemented in one or more of the elements of systems 400, 500,
700, and 800.
Further, it is to be appreciated that processing system 100 may
perform at least part of the method described herein including, for
example, at least part of methods 200, 300, and 600 of FIGS. 2, 3,
and 6, respectively. Similarly, part or all of system 200 may be
used to perform at least part of methods 200, 300, and 600 of FIGS.
2, 3, and 6, respectively.
Referring now to FIG. 2, a block/flow diagram of a method for
dynamically controlling grid-scale Energy Storage Systems (ESSs)
using hybrid energy management 200 is illustratively depicted in
accordance with an embodiment of the present principles. In an
embodiment, the method 200 may be employed to determine an optimal
Grid Scale Storage (GSS) schedules to participate in day-ahead
energy and Frequency Regulation (FR) markets, and to control
voltage regulation services in real-time. A plurality of parameters
related to energy market, network, and/or GSS operations may be
measured of received according to various embodiments, and may be
employed as input for a hybrid GSS management method 200 according
to the present principles. The GSS management method according to
the present principles is considered hybrid because of the combined
(e.g., simultaneous) use of the primary and tertiary controls, and
enables an ESS to be employed for multiple applications/purposes
simultaneously according to various embodiments.
To participate in day-ahead markets, users of GSS units may submit
energy bids to market operators prior to the beginning of each day.
Thus, the method 200 may determine optimal bids (e.g., energy
demands; requirements; requests; etc.) by, for example, performing
co-optimization (e.g., optimizing in a plurality of markets (e.g.,
energy market, frequency regulation market, voltage regulation
market, etc.) rather than optimizing just a single market) with
dynamic constraints in block 209 for the next day. These bids may
be based on, for example, forecasted market prices and/or estimated
reserve capacity for voltage regulation operation according to
various embodiments of the present principles.
In an embodiment, historical Independent System Operator (ISO)
price data 202, historical and/or forecasted load and/or generation
profiles/data 204 for the next day may be employed as input for
time series modeling (e.g., Locational Marginal Price (LMP) time
series modeling) in block 205 to forecast market prices according
to the present principles. In an embodiment, a time series based
method (e.g., Auto Regressive Moving Average with eXogeneous inputs
(ARMAX), Auto-Regressive eXogeneous (ARX), etc.) may be employed
for forecasting day-ahead electricity market prices in blocks 205
and 207. The time series modeling in blocks 205 and 207 will be
discussed in further detail herein below.
In an embodiment, historical voltage profiles and voltage
regulation requirements (e.g., at the point of GSS connection to
the energy grid) 206 may be employed as input for time series
modeling (e.g., Voltage Regulation (VR) time series modeling) in
block 207 to determine (e.g., estimate) the necessary (or desired)
ESS capacity for voltage regulation during each hour of the next
day. The VR time series modeling 206 will be described in further
detail herein below.
In an embodiment, the estimated LMP from block 205 and the
estimated GSS/battery capacity (e.g., voltage regulation capacity)
from block 207 may be employed as input for performing GSS/battery
co-optimization with dynamic constraints using an optimizer in
block 209. In an embodiment, GSS/battery cost and operation limits
208 may also be employed as input into an optimizer for performing
co-optimization in block 209. The co-optimization in block 209 may
determine optimal GSS bids for day-ahead market operation, and the
bids may be generated and submitted (e.g., daily) to one or more
market operators in block 216 according to the present
principles.
In block 209, for simplicity of illustration of the co-optimizing,
it may be assumed that a GSS unit is a price taker in both energy
and FR markets according to an embodiment, although co-optimization
may be performed in block 209 when a GSS unit is not a price taker
in both energy and FR markets according to various embodiments. In
an embodiment, the hourly revenue for the GSS unit from the markets
(Rev) may be determined based on forecasted day-ahead LMP and FR
market prices from blocks 205 and/or 207 as follows:
Rev(k)=LMP(k)P.sub.b.sup.energy(k)+.lamda..sup.reg(k)P.sub.b.sup.reg(k),
(1) where P.sub.b.sup.energy, .lamda..sup.reg, and P.sub.b.sup.reg
represent hourly GSS power in an energy market, forecasted FR
price, and GSS capacity in the FR market, respectively. In an
embodiment, it may be assumed that the GSS provides equal
regulation of up and down capacity during each hour of operation in
the FR market. In an embodiment, the up and down capacity may
include frequency regulation up and frequency regulation down
capacities, which may be the same as the GSS schedule for the FR
market. For simplicity of illustration, they may be assumed to be
equal, and may be determined in the co-optimization by solving for
P.sub.b.sup.reg in (1). Therefore, only one FR regulation capacity
and one FR price for each hour may be considered in (1) by the
optimizer in block 209 according to an embodiment of the present
principles.
In an embodiment, the co-optimization in block 209 may include
modeling the hourly degradation cost using a battery
life/degradation cost determination device according to the present
principles. To model the hourly degradation cost of a GSS unit when
participating in the market (Cost), and average model based on per
unit degradation cost of GSS multiplied by its energy throughput
may be generated as follows:
Cost(k)=C.sub.b|P.sub.b.sup.energy(k)|+C.sub.bP.sub.b.sup.reg(k),
(2) where C.sub.b represents GSS per unit degradation cost. In an
embodiment, scheduled power in the energy market may assume both
positive and negative values (e.g., depending on the
charge/discharge state of the GSS), while the scheduled capacity in
the FR market may always be a positive value. Thus, the absolute
value function may be applied only to the first term of the
rate-harmonized scheduling (RHS) described in (2).
In an embodiment, actual degradation cost of a GSS unit may be less
than the value determined using (1) because of two reasons. First,
only a fraction of reserved capacity in the FR market is generally
deployed during real-time operation. Second, desired GSS power in
the energy and FR markets at each particular time instant may be in
an opposite direction (e.g., the GSS power schedule for the energy
market could be positive (discharge) while the GSS power schedule
for the frequency regulation market is negative (charging)), and
the total GSS power may be the sum of GSS power in the energy
market and the GSS power in the frequency regulation market. This
may reduce the total output power of the GSS and its degradation
cost in some embodiments.
In an embodiment, the objective function of market co-optimization
in block 209 may be considered in terms of daily net revenue as
follows:
.times..times..times..times..function..function. ##EQU00001## which
may be subject to constraints related to limited energy and power
capacity of the GSS unit as follows:
SoC.sub.min(k).ltoreq.SoC(k).ltoreq.SoC.sub.max(k) (4)
-P.sub.b.sup.max(k).ltoreq.P.sub.b.sup.energy(k).ltoreq.P.sub.b.sup.max(k-
) (5)
P.sub.b.sup.energ(k)+P.sub.b.sup.reg(k).ltoreq.P.sub.b.sup.max(k)
(6) -P.sub.b.sup.energy(k)+P.sub.b.sup.reg(k)
.ltoreq.P.sub.b.sup.max(k), (7) where SoC and P.sub.b.sup.max
represent the GSS state of charge and the GSS maximum power,
respectively.
In an embodiment, GSS state of charge may be determined as
follows:
.function..function..function. ##EQU00002## where E.sub.b
represents the energy capacity of the GSS. The maximum and minimum
values of the state of charge in (4), and the maximum GSS power in
(6), (7), and (8) may be dependent on time (k) so that they may be
adjusted dynamically (e.g., adjusted every hour) based on voltage
regulation requirements using a voltage regulator in block 211
according to an embodiment of the present principles.
In an embodiment, multiple market co-optimization may be performed
in block 209, and a sample objective function for day-ahead energy
and regulation markets may be illustrated as follows:
.times..times..times..times..times..times..function..times..function..fun-
ction..times..function..times..times..function..times..times..times..funct-
ion..times..function..function..times..times..function.
##EQU00003## where
.times..times..times..times..times..times..function..times..function.
##EQU00004## represents the energy market arbitrage,
C.sup.ru[h].times.P.sub.battery.sup.ru[h] represents regulation
up-revenue, C.sup.rd[h].times.P.sub.battery.sup.rd[h] represents
regulation-down revenue, and
C.sub.b.times.|P.sub.battery.sup.EM[h]+P.sub.battery.sup.ru[h]+P.sub.batt-
ery.sup.rd[h]|) represents battery wear cost. In an embodiment, the
energy arbitrage market, regulation-up revenue, regulation-down
revenue, and battery wear cost may be subject to dynamic
constraints/limits (e.g., state-of-charge (SOC) limits, maximum
charge and discharge power limits, etc.), and the consideration of
the battery wear cost may enable maximization of total net revenue
according to the present principles.
In an embodiment, after the optimal GSS hourly schedule for market
operation is generated by the optimizer in block 209, the generated
schedule may be employed for adaptive voltage regulation and GSS
dispatching in block 211. In an embodiment, second-by-second
control of the GSS unit during the day according to GSS market
schedule and voltage regulation requirements may be performed in
block 211 using, for example, a voltage regulator according to the
present principles. Network voltage/current and GSS/battery
parameters may be measured at each time-step in blocks 210 and 212,
respectively, and employed as input for block 211. In block 214,
real-time charge and discharge commands may be sent to the GSS unit
to control participation in the voltage regulation market (e.g.,
second-by-second control) in block 218 according to an embodiment
of the present principles. The voltage regulation using will be
described in further detail herein below.
Referring now to FIG. 3, an exemplary method for forecasting 300 is
illustratively depicted in accordance with an embodiment of the
present principles. In an embodiment, the method 300 may employ two
steps for forecasting day-ahead prices in electricity markets. The
first step may include processing inputs, including, historical
load and/or generation values (e.g., 2-3 days) from block 302,
forecasted load and/or generation values (e.g., 2-3 days) from
block 304, and historical price data (e.g., LMP, etc.) from block
308. In another embodiment, forecasting may not be a two-step
process, and may employ a plurality of signals and their functions
as exogeneous inputs during processing.
In an embodiment, inputs (e.g., 302, 304) may be processed using
various functions to obtain the actual input signals to the models
(e.g., ARMAX models) in block 306. The particular function choice
for processing load and/or generation variables (e.g., using log,
absolute value, derivatives, etc.) in block 306 may be dependent on
the particular price that is to be forecasted. For example, LMP is
highly dependent on the load forecasts from block 304 and the times
at which the load forecast reaches maximum and minimum values
(which may be determined through derivatives). In an embodiment, to
forecast frequency regulation prices, functions such as absolute
value may be employed in block 306 according to the present
principles.
In an embodiment, the inputs processed in block 306 may be employed
as input for the time series modeling (e.g., time series
forecasting) in block 310. An illustrative example of a time series
model according to an embodiment is the following:
P(t+1)=a.sub.1P(t)+a.sub.2P(t-1)+a.sub.3P(t-2)+b.sub.1P(t)+b.sub.2P(t-1)+-
c.sub.1X(t)+c.sub.2X(t-1)+.epsilon.(t), (10) where P is a price
(e.g., LMP, frequency regulation price), and P is a moving average
considering a fixed number of steps back. The price forecast
(P(t+1)) may also be a function of the past (e.g., historical)
values of exogenous inputs (X(t), (X(t-1)). In an embodiment,
unique exogenous inputs which are functions of historical values of
load and generation 302, as well as functions of load forecasts 304
may be employed during time series modeling (e.g., ARX, ARMAX,
etc.) in block 310 to generate day-ahead forecasted prices in block
312 according to the present principles.
Referring now to FIG. 4, an exemplary power system and a simplified
equivalent model 400 are illustratively depicted in accordance with
an embodiment of the present principles. In an embodiment, the
power system 400 may include one or more photovoltaic (PV) farms
702, and one or more grid scale energy storage systems (GSESSs)
404. Voltage regulation may be employed to control the real and
reactive power injection at points of common coupling (PCCs) 405 by
dispatching GSS. One or more voltage sources 408 may be employed
according to various embodiments.
In an embodiment, the power system representation may be simplified
as, for example, a Thevenin equivalent, by performing equivalencing
in block 409. After equivalencing, the simplified system is
depicted, and is a complex variable representing voltage of the
equivalent source in block 418, Z (complex) is the impedence of the
Thevenin equivalent in block 416, is the current in block 417, and
V (complex) is the voltage phasor at the PCC 415. In an embodiment,
, P, and Q are current, phasor, active power injection, and
reactive power injection from GSS (or ESS) 414 into the grid,
respectively. In various embodiments, the above variables may
constantly be changing as system operating conditions change.
Referring now to FIG. 5, with continued reference to FIG. 4, a high
level diagram of an exemplary system and method for voltage
regulation 500 is illustratively depicted in accordance with an
embodiment of the present principles. In an embodiment, a voltage
regulator 511 may be employed for voltage regulation (e.g., control
the real and reactive power injection at a point of common coupling
(PCC) by dispatching GSSs. In an illustrative embodiment which may
employ a Kalman filter in block 506, dynamic system modeling may be
performed in block 502 (e.g., using V, , Z, and described with
reference to FIG. 3) by the following method: V= +Z (11) Define the
following: =E.sub.R+jE.sub.I (12) V=V.sub.R+jV.sub.I (13) Z=R+jX
(14) =I.sub.R+jI.sub.X, (15) where= {square root over (-1)}. In an
embodiment, equation (11) may be broken up into two real equations
during system modeling 502, which, in matrix format, may be shown
by the following:
##EQU00005##
In an embodiment, during system modeling 502, voltage and current
injection at a PCC can be measured, and therefore V.sub.R, V.sub.I,
I.sub.R and I.sub.I may be considered as known variables while
E.sub.R, E.sub.I, R, and X may be parameters to estimate. To solve
two equations with four unknowns, at least two measurement points
are employed. In an embodiment, a sliding window containing four
measurement points may be employed for the parameter estimation
according to the present principles.
In an embodiment, a filter (e.g., Kalman filter) may be employed
during system modeling in block 502 and time window (e.g., time
step) determination in block 504. A Kalman filter is an optimal
state estimator for dynamical systems. It may be employed to
estimate the system unknown states efficiently in a recursive way.
A general discrete state-space representation of a dynamic system
may be represented as follows: x.sub.k+1=A.sub.kx.sub.k+w.sub.k
(17) z.sub.k=H.sub.kx.sub.k+V.sub.k, (18) where x.sub.k is the
state vector; A.sub.k is the state transition matrix; Z.sub.k is
the measurement vector; H.sub.k is the observation matrix; w.sub.k
and v.sub.k are the process noise and measurement noise. In an
embodiment, noise w.sub.k and v.sub.k may be assumed to be
independent of each other, and their covariance matrixes may be
given by the following: E(w.sub.kw.sub.k.sup.T)=R.sub.k (19)
E(v.sub.kv.sub.k.sup.T)=Q.sub.k (20)
In block 504, the vectors/matrices from equations (17) and (18) may
be employed to determine the time window (e.g., time step), and may
be defined as follows:
.times..times..times..times..times..times..times..times..times..times..ti-
mes..times..times..times..times..times..times..times..times..times..times.-
.times..times..times..times..times..times..times..times.
##EQU00006## where (.).sup.k refers to the unknown parameters at
the k th time step (window), and (.).sup.ki refers to the i th
measurement point at the k th time step (window).
In an embodiment, recursive iteration using, for example, a Kalman
filter, may be performed in block 506, and unknown parameters at
each time instant (e.g., from block 504) may be determined
according to the present principles using the following recursive
method: P.sub.k+1=A.sub.k+1P.sub.kA.sub.k+1.sup.T+Q.sub.k (25)
K.sub.k+1=P.sub.k+1H.sub.k+1.sup.T[H.sub.k+1P.sub.k+1H.sub.k+1.sup.T+R.su-
b.k+1].sup.-1 (26) x.sub.k+1=A.sub.k+1x.sub.k+K.sub.k+1[z.sub.k+1-H
.sub.k+1A.sub.kx.sub.k] (27)
P.sub.k+1+P.sub.k+1-K.sub.k+1H.sub.k+1P.sub.k+1, (28) where K.sub.k
is the Kalman gain at time step k.
In an embodiment, after calculating the Thevenin equivalent
impedance at a PCC using recursive iteration in block 506, the
power needed to keep voltage within a required (or desired) limit
(e.g., injection limits) may be determined in block 508 according
to the present principles. The change in voltage caused by
variation in injections may be determined by the following
method:
.DELTA..times..times..DELTA..times..times..times..times..DELTA..times..ti-
mes..times..times. ##EQU00007## where V* is the conjugate of
variable V. Therefore, based on the operating condition (SOC, power
factor, etc.) of grid scale energy storage, the power injection can
be adjusted to regulate the voltage at PCC according to various
embodiments of the present principles.
Referring now to FIG. 6, with continued reference to FIGS. 4 and 5,
an exemplary method for voltage regulation 600 is illustratively
depicted in accordance with an embodiment of the present
principles. In an embodiment, after determining the maximum power
injection possible at a particular PCC in block 508, a voltage
violation margin may be defined as the difference between the power
injection limits and the actual power injection (determined in
block 608), the output of which may be employed for voltage
regulation using the voltage regulator 511.
In an embodiment, the maximum power injection at a particular PCC
is input in block 602, and an initial time step (e.g., t=t.sub.k)
may be set in block 604. Parameters may be evaluated (e.g., for a
Thevenin equivalent) using, for example, a Kalman filter in block
506, and the voltage violation margin (e.g.,
P.sub.lim.sub._.sub.upper and P.sub.lim.sub._.sub.lower) may be
calculated in block 608. In block 610, if
P.sub.marg.sub._.sub.upper<0, then it is determined whether a
particular ESS is in charging mode in block 614. If yes, charging
may be increased by -P.sub.marg.sub._.sub.upper+.epsilon. in block
618, where .epsilon. represents a small positive number, and is
employed to ensure that voltage is restored to a normal (e.g.,
acceptable) range (e.g., 0.9 pu to 1.1 pu). If no, then discharging
may be decreased by -P .sub.marg.sub._.sub.upper+.epsilon. in block
620. A next time step (e.g., t=t.sub.k+.DELTA.t) may be determined
in block 626, a new time step may be set in block 604, and the
method may iteratively repeat to continually track system voltage
to ensure the system voltage is within a normal (e.g., acceptable)
range.
In an embodiment, if P.sub.marg.sub._.sub.upper is determined to
not be less than 0 in block 610, then it is determined whether
P.sub.marg.sub.--lower<0 in block 612. If yes, then it may be
determined whether a particular ESS is in charging mode in block
616. If yes, charging may be decreased by
-P.sub.marg.sub._.sub.lower+.epsilon. in block 622. If no, then
discharging may be increased by
-P.sub.marg.sub._.sub.lower+.epsilon. in block 624. If
P.sub.marg.sub._.sub.lower is determined to be greater than 0 in
block 612, then a next time step (e.g., t=t.sub.k+.DELTA.t) may be
determined in block 626, a new time step may be set in block 604,
and the method may iteratively repeat until a particular threshold
has been met according to various embodiments.
Referring now to FIG. 7, an exemplary system and method for primary
and tertiary control of Energy Storage Systems (ESSs) 700 is
illustratively depicted in accordance with an embodiment of the
present principles. In an embodiment, one or more ESSs may be
controlled using a primary controller 706 and a tertiary controller
704 for optimal battery dispatch and/or automated power injection
for voltage regulation in accordance with the present
principles.
In an embodiment, a tertiary controller 704 may be employed for
optimization and determining and submitting daily bids for
day-ahead energy markets. Independent System Operator (ISO) data
may be input in block 701, and a forecaster 708 may be employed for
time series forecasting (e.g., ARX forecasting) to determine a
profile of future ISO signals. The output of the forecaster 708 and
historical battery data 703 may be employed as input to a life
estimator 710 to determine an optimal battery dispatch schedule.
The life estimator may calculate the life impact of the providing
of the forecasted ISO service, and the output of the life estimator
may be employed for optimization (e.g., stochastic dispatch
optimization) in block 712. Stochastic optimization in block 712
may evaluate a cost tradeoff of providing the ISO service bs.
battery life cost for a plurality of situations to determine the
optimal battery dispatch according to the present principles. The
output of the optimization in block 712 may be employed for command
controlling/dispatching in block 714.
In an embodiment, the forecaster 708 may employ auto-regressive
exogenous service forecasting. Offline simulations may be employed
to estimate optimal battery size (e.g., if not provided by service
provider), and the life estimator 710 may evaluate the real-time
cost of all services, enabling the services to be provided in the
most economical way without compromising battery life. The life
estimator 710 may ascertain safe operating parameters and discharge
limits to generate auction bids and derive favorable operational
conditions for the battery system. The forecaster 708 and life
estimator 710 may be employed to proactively provision battery
resources between usable and reserved allocations for day-ahead,
hour-ahead, and real-time markets according to various embodiments
using the market-aware controllers 704, 706 according to the
present principles.
In an embodiment, a primary controller 706 may be employed for
second by second control of participation in a voltage regulation
market. In an embodiment, a monitoring device may be employed to
monitor real-time conditions within an energy system. In an
embodiment, the primary controller 706 may include a real-time
system parameter estimator 720 based on Kalman filtering that
enables millisecond level control for voltage regulation. In an
embodiment, an active power controller 716 may dispatch ESS
automatically to maintain voltage within a normal range, and a
reactive power controller 716 may dispatch ESS's reactive power
output to support a power system. A voltage regulator 718 may
control ESS for conservation voltage regulation (CVR) to reduce
system loss and save energy according to various embodiments of the
present principles.
In an embodiment, reliability of energy systems may be improved
using the life estimator 710 and forecaster 708, and high speed
operation without remote communication may be enabled. Forecasting
and life estimation enables proactive provisioning of battery to
preempt failures due to accelerated degradation. Maintaining a
stable voltage also improves system reliability. High speed primary
control 706 using parameter identification in block 720 enables
automated operation during loss of remote communication. While most
failures in the past have occurred in the inverters and power
electronics, and conventional systems cannot prevent such failures,
the present principles may be employed to track life effects and
tailor performance so as to prevent such failures by delivering
reliable service consistently according to various embodiments of
the present principles.
FIG. 8 shows an exemplary system for dynamically controlling
grid-scale Energy Storage Systems (ESSs) 800, with continued
reference to FIG. 2, in accordance with an embodiment of the
present principles. While many aspects of system 800 are described
in singular form for the sakes of illustration and clarity, the
same can be applied to multiples ones of the items mentioned with
respect to the description of system 800. For example, while a
single voltage regulator 806 is described, more than one voltage
regulator 806 can be used in accordance with the teachings of the
present principles, while maintaining the spirit of the present
principles. Moreover, it is appreciated that the voltage regulator
is but one aspect involved with system 800 than can be extended to
plural form while maintaining the spirit of the present
principles.
The system 800 can include a forecaster 802, an optimizer 804, a
voltage regulator 806, a measurement device 808, a storage device
810, a controller 812, a battery life/degradation cost determiner
814, and a time series modeler 816.
In an embodiment, the forecaster 802 may forecast load and/or
generation profiles/data for day-ahead energy markets (as described
above with reference to FIG. 2), and the forecasts may be stored in
a storage device 810, and may be input into a time series modeler
816 for LMP and/or voltage regulation time series modeling (as
described above with reference to FIG. 2) according to various
embodiments.
In an embodiment, a battery life/degradation cost determiner 208
may be employed to determine GSS/battery costs and operation
limits, and the output of the time series modeler 816 and the
determiner 814 may be employed as input for co-optimization (as
described above with reference to FIG. 2) using the optimizer 804.
A measurement device 808 may be employed to take, for example,
battery, and network voltage and current measurements. The
measurements (e.g., 210, 212) and the output of the optimizer 804
may be employed by the voltage regulator 806 for voltage regulation
(as described above with reference to FIG. 2) according to various
embodiments of the present principles. In an embodiment, the
controller 812 may control distribution of energy to or from the
one or more ESSs based on the optimal bids generated by the
optimizer 804. In some embodiments, the controller 812 may be a
virtual appliance (e.g., computing device, node, server, etc.), and
may be directly connected to an ESS or located remotely for
controlling via any type of transmission medium (e.g., Internet,
intranet, internet of things, etc.). In some embodiments, the
controller 812 may be a hardware device, and may be attached to an
ESS or built into an ESS according to the present principles.
In the embodiment shown in FIG. 8, the elements thereof are
interconnected by a bus 801. However, in other embodiments, other
types of connections can also be used. Moreover, in an embodiment,
at least one of the elements of system 800 is processor-based.
Further, while one or more elements may be shown as separate
elements, in other embodiments, these elements can be combined as
one element. The converse is also applicable, where while one or
more elements may be part of another element, in other embodiments,
the one or more elements may be implemented as standalone elements.
These and other variations of the elements of system 800 are
readily determined by one of ordinary skill in the art, given the
teachings of the present principles provided herein, while
maintaining the spirit of the present principles.
The foregoing is to be understood as being in every respect
illustrative and exemplary, but not restrictive, and the scope of
the invention disclosed herein is not to be determined from the
Detailed Description, but rather from the claims as interpreted
according to the full breadth permitted by the patent laws.
Additional information is provided in an appendix to the
application entitled, "Additional Information". It is to be
understood that the embodiments shown and described herein are only
illustrative of the principles of the present invention and that
those skilled in the art may implement various modifications
without departing from the scope and spirit of the invention. Those
skilled in the art could implement various other feature
combinations without departing from the scope and spirit of the
invention.
* * * * *